Empirical Aspect of Big Data to Enhance Medical Images Using HIPI

2018 ◽  
Author(s):  
Akriti Maheshwari ◽  
Yogesh Gupta
Keyword(s):  
Big Data ◽  
Medical Images

scholarly journals Big Data System for Medical Images Analysis

2020 ◽  
Author(s):  
Janusz Bobulski ◽  
Mariusz Kubanek
Keyword(s):  
Big Data ◽  
Medical Images ◽  
Processing System ◽  
Large Data ◽  
Data Systems ◽  
Computer Data ◽  
Fast Processing ◽  
Big Data Systems

Big Data in medicine contains conceivably fast processing of large data volumes, alike new and old in perseverance associate the diagnosis and treatment of patients’ diseases. Backing systems for that kind activities may include pre-programmed rules based on data obtained from the medical interview, and automatic analysis of test diagnostic results will lead to classification of observations to a specific disease entity. The current revolution using Big Data significantly expands the role of computer science in achieving these goals, which is why we propose a computer data processing system using artificial intelligence to analyse and process medical images. We conducted research that confirms the need to use GPUs in Big Data systems that process medical images. The use of this type of processor increases system performance.

Artificial Intelligence and Big Data

2020 ◽  
Vol 237 (12) ◽  
pp. 1438-1441
Author(s):  
Soenke Langner ◽  
Ebba Beller ◽  
Felix Streckenbach
Keyword(s):  
Artificial Intelligence ◽  
Image Analysis ◽  
Big Data ◽  
Deep Learning ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Analysis ◽  
Clinical Applications ◽  
Radiological Imaging ◽  
Learning Techniques

AbstractMedical images play an important role in ophthalmology and radiology. Medical image analysis has greatly benefited from the application of “deep learning” techniques in clinical and experimental radiology. Clinical applications and their relevance for radiological imaging in ophthalmology are presented.

Evolution of Big Data in Medical Imaging Modalities to Extract Features Using Region Growing Segmentation, GLCM, and Discrete Wavelet Transform

2022 ◽  
pp. 455-482
Author(s):  
Yogesh Kumar Gupta
Keyword(s):  
Big Data ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Medical Images ◽  
Human Life ◽  
Image Data ◽  
Region Growing ◽  
Discrete Wavelet ◽  
Promising Area ◽  
Region Growing Segmentation

Big data refers to the massive amount of data from sundry sources (gregarious media, healthcare, different sensor, etc.) with very high velocity. Due to expeditious growth, the multimedia or image data has rapidly incremented due to the expansion of convivial networking, surveillance cameras, satellite images, and medical images. Healthcare is the most promising area where big data can be applied to make a vicissitude in human life. The process for analyzing the intricate data is mundanely concerned with the disclosing of hidden patterns. In healthcare fields capturing the visual context of any medical images, extraction is a well introduced word in digital image processing. The motive of this research is to present a detailed overview of big data in healthcare and processing of non-invasive medical images with the avail of feature extraction techniques such as region growing segmentation, GLCM, and discrete wavelet transform.

scholarly journals A lossless compression method for multi-component medical images based on big data mining

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Gangtao Xin ◽  
Pingyi Fan
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Medical Images ◽  
Lossless Compression ◽  
Disease Diagnosis ◽  
Compression Algorithm ◽  
Medical Systems ◽  
Big Data Mining ◽  
Set Up ◽  
Local Storage

AbstractIn disease diagnosis, medical image plays an important part. Its lossless compression is pretty critical, which directly determines the requirement of local storage space and communication bandwidth of remote medical systems, so as to help the diagnosis and treatment of patients. There are two extraordinary properties related to medical images: lossless and similarity. How to take advantage of these two properties to reduce the information needed to represent an image is the key point of compression. In this paper, we employ the big data mining to set up the image codebook. That is, to find the basic components of images. We propose a soft compression algorithm for multi-component medical images, which can exactly reflect the fundamental structure of images. A general representation framework for image compression is also put forward and the results indicate that our developed soft compression algorithm can outperform the popular benchmarks PNG and JPEG2000 in terms of compression ratio.

scholarly journals Big Data System for Medical Images Analysis

2020 ◽  
Author(s):  
Janusz Bobulski ◽  
Mariusz Kubanek
Keyword(s):  
Big Data ◽  
Medical Images ◽  
Processing System ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Computer Data ◽  
Fast Processing

Big Data in medicine includes possibly fast processing of large data sets, both current and historical in purpose supporting the diagnosis and therapy of patients' diseases. Support systems for these activities may include pre-programmed rules based on data obtained from the interview medical and automatic analysis of test results diagnostic results will lead to classification of observations to a specific disease entity. The current revolution using Big Data significantly expands the role of computer science in achieving these goals, which is why we propose a Big Data computer data processing system using artificial intelligence to analyze and process medical images.

Evolution of Big Data in Medical Imaging Modalities to Extract Features Using Region Growing Segmentation, GLCM, and Discrete Wavelet Transform

2021 ◽  
pp. 41-78
Author(s):  
Yogesh Kumar Gupta
Keyword(s):  
Big Data ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Medical Images ◽  
Human Life ◽  
Image Data ◽  
Region Growing ◽  
Discrete Wavelet ◽  
Promising Area ◽  
Region Growing Segmentation

Big data refers to the massive amount of data from sundry sources (gregarious media, healthcare, different sensor, etc.) with very high velocity. Due to expeditious growth, the multimedia or image data has rapidly incremented due to the expansion of convivial networking, surveillance cameras, satellite images, and medical images. Healthcare is the most promising area where big data can be applied to make a vicissitude in human life. The process for analyzing the intricate data is mundanely concerned with the disclosing of hidden patterns. In healthcare fields capturing the visual context of any medical images, extraction is a well introduced word in digital image processing. The motive of this research is to present a detailed overview of big data in healthcare and processing of non-invasive medical images with the avail of feature extraction techniques such as region growing segmentation, GLCM, and discrete wavelet transform.

scholarly journals Towards in silico prognosis using big data

2016 ◽  
Vol 2 (1) ◽  
pp. 57-60 ◽  
Author(s):  
Nicholas Ohs ◽  
Fabian Keller ◽  
Ole Blank ◽  
Yuk-Wai Wayne Lee ◽  
Chun-Yiu Jack Cheng ◽  
...  
Keyword(s):  
Physical Activity ◽  
Vitamin D ◽  
Big Data ◽  
Bone Density ◽  
In Silico ◽  
Medical Images ◽  
Distal Tibia ◽  
Complex Diseases ◽  
Patient Specific ◽  
Element Analysis

AbstractClinical diagnosis and prognosis usually rely on few or even single measurements despite clinical big data being available. This limits the exploration of complex diseases such as adolescent idiopathic scoliosis (AIS) where the associated low bone mass remains unexplained. Observed low physical activity and increased RANKL/OPG, however, both indicate a mechanobiological cause. To deepen disease understanding, we propose an in silico prognosis approach using clinical big data, i.e. medical images, serum markers, questionnaires and live style data from mobile monitoring devices and explore the role of inadequate physical activity in a first AIS prototype. It employs a cellular automaton (CA) to represent the medical image, micro-finite element analysis to calculate loading, and a Boolean network to integrate the other biomarkers. Medical images of the distal tibia, physical activity scores, and vitamin D and PTH levels were integrated as measured clinically while the time development of bone density and RANKL/OPG was observed. Simulation of an AIS patient with normal physical activity and patient-specific vitamin D and PTH levels showed minor changes in bone density whereas the simulation of the same AIS patient but with reduced physical activity led to low density. Both showed unchanged RANKL/OPG and considerable cortical resorption. We conclude that our integrative in silico approach allows to account for a variety of clinical big data to study complex diseases.

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